
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import tensorflow as tf
import cv2
import keras
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import array_to_img


train_path ='/content/drive/MyDrive/Capstone Data/Train Images'
test_path ='/content/drive/MyDrive/Capstone Data/Test Images'


def display_images():
  category = os.listdir(train_path)
  for category in category:
        image = load_img(os.path.join(train_path,category,os.listdir(os.path.join(train_path,category))[0]))
        image = image.resize([100,100])
        print('category:',category)
        plt.imshow(image)
        plt.show()

display_images()

category: Acura TL Type-S 2008

category: AM General Hummer SUV 2000

category: Acura ZDX Hatchback 2012

category: Aston Martin V8 Vantage Convertible 2012

category: Acura RL Sedan 2012

category: Aston Martin Virage Convertible 2012

category: Acura TL Sedan 2012

category: Acura TSX Sedan 2012

category: Aston Martin V8 Vantage Coupe 2012

category: Acura Integra Type R 2001


train_df = pd.read_csv('/content/drive/MyDrive/Capstone Data/Annotations/Train Annotations.csv',header = None,names = ['file','x_min','y_min','x_max','y_max','class'])


train_df


test_df = pd.read_csv('/content/drive/MyDrive/Capstone Data/Annotations/Test Annotation.csv',header = None,names = ['file','x_min','y_min','x_max','y_max','class'])


test_df


# Train images with its classification
classification=[]
category = os.listdir(train_path)
for category in category:
    for i in range(0,len(os.listdir(os.path.join(train_path,category)))):
        image = os.listdir(os.path.join(train_path,category))[i]
        classification.append([image,category])


#converting list to dataframe
train_classification = pd.DataFrame(classification,columns=['file','classification'])


train_classification


# Test images with its classification
classification2=[]
category2 = os.listdir(test_path)
for category in category2:
    for i in range(0,len(os.listdir(os.path.join(test_path,category)))):
        image = os.listdir(os.path.join(test_path,category))[i]
        classification2.append([image,category])


#converting list to dataframe
test_classification = pd.DataFrame(classification2,columns=['file','classification'])


test_classification


# Merging the data frames
train_df_final = train_df.merge(train_classification, on="file", how = 'inner')


train_df_final


#adding file path in the dataframe
train_df_final['file_path']=train_path+'/'+train_df_final['classification']+'/'+train_df_final['file']


train_df_final


test_df_final = test_df.merge(test_classification, on="file", how = 'inner')


test_df_final


test_df_final['file_path']=test_path+'/'+test_df_final['classification']+'/'+test_df_final['file']


test_df_final


# Adding the height & width field with the dataframe
#train_df_final['width'] = 0
#train_df_final['height'] = 0


# extracting the image width & height and loading to dataframe
#for i in range(0,train_df_final['file_path'].shape[0]):
 # image = load_img(train_df_final['file_path'][i])
  #train_df_final['width'][i]=image.size[0]
  #train_df_final['height'][i]=image.size[1]


#train_df_final


#train_df_final['file_path'].shape[0]


#train_df_final.to_excel("train_df_final_caps.xlsx")


train_df_final= pd.read_excel('/content/drive/MyDrive/Capstone Data/train_df_final_caps.xlsx')


train_df_final


# Adding the height & width field with the dataframe
#test_df_final['width'] = 0
#test_df_final['height'] = 0
# extracting the image width & height and loading to dataframe
#for i in range(0,test_df_final['file_path'].shape[0]):
 # image = load_img(test_df_final['file_path'][i])
  #test_df_final['width'][i]=image.size[0]
  #test_df_final['height'][i]=image.size[1]
  #print(i)


#test_df_final


#test_df_final.to_excel("test_df_final_caps.xlsx")


test_df_final= pd.read_excel('/content/drive/MyDrive/Capstone Data/test_df_final_caps.xlsx')


test_df_final


# Display images with bounding box
img_num = np.random.randint(0,train_df_final.shape[0])
img = cv2.imread(train_df_final.loc[img_num,'file_path'])
img = cv2.resize(img,(500,500))
w = train_df_final.loc[img_num,'width']
h = train_df_final.loc[img_num,'height']
cv2.rectangle(img,
              (int(train_df_final.loc[img_num,'x_min']*500/w),int(train_df_final.loc[img_num,'y_min']*500/h)),
              (int(train_df_final.loc[img_num,'x_max']*500/w),int(train_df_final.loc[img_num,'y_max']*500/h)),(0,255,0),2)

img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
plt.suptitle(train_df_final.loc[img_num,'classification'])
plt.imshow(img)
plt.show()


def batch_generator(df,batch_size=32):
  while True:
    image_nums = np.random.randint(0,df.shape[0],size=batch_size)
    batch_images = np.zeros(shape=(batch_size,img_size,img_size,3))
    batch_labels = np.zeros(shape=(batch_size,196))
    batch_bboxes = np.zeros(shape=(batch_size,4))

    for i in range(batch_size):
      img=tf.keras.preprocessing.image.load_img(df.loc[image_nums[i],'file_path'],
                                                target_size=(img_size,img_size))
      
      img_array = tf.keras.preprocessing.image.img_to_array(img)
      batch_images[i] = img_array

      c1_label = df.loc[image_nums[i],'class']
      c1_label = tf.keras.utils.to_categorical(c1_label,196)
      batch_labels[i]=c1_label

      img_width = df.loc[image_nums[i],'width']
      img_height = df.loc[image_nums[i],'height']

      xmin = df.loc[image_nums[i],'x_min']*img_size/img_width
      xmax = df.loc[image_nums[i],'x_max']*img_size/img_width

      ymin = df.loc[image_nums[i],'y_min']*img_size/img_width
      ymax = df.loc[image_nums[i],'y_max']*img_size/img_width

      batch_bboxes[i]=[xmin,ymin,xmax-xmin,ymax-ymin]

      for i in range(batch_size):
        batch_images[i]=tf.keras.applications.vgg16.preprocess_input(batch_images[i])
      
      batch_bboxes=batch_bboxes/img_size

      yield batch_images,[batch_labels,batch_bboxes]


#building the model
img_size = 224
tf.keras.backend.clear_session()
model = tf.keras.applications.VGG16(include_top=False,
                                    input_shape=(img_size,img_size,3),weights='imagenet')

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
58892288/58889256 [==============================] - 0s 0us/step


model.summary()

Model: "vgg16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________


len(model.layers)

19


for layer in model.layers:
  layer.trainable=False


model.summary()

Model: "vgg16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
=================================================================
Total params: 14,714,688
Trainable params: 0
Non-trainable params: 14,714,688
_________________________________________________________________


model.output

<KerasTensor: shape=(None, 7, 7, 512) dtype=float32 (created by layer 'block5_pool')>


x1 = model.output
x2 = tf.keras.layers.GlobalAveragePooling2D()(x1)
x3 = tf.keras.layers.Dropout(0.5)(x2)
x4 = tf.keras.layers.Dense(200,activation='relu')(x3)
x5 = tf.keras.layers.BatchNormalization()(x4)


label_output = tf.keras.layers.Dense(196,
                                     activation = 'softmax',
                                     name='class_op')(x5)


bbox_output = tf.keras.layers.Dense(4,
                                     activation = 'sigmoid',
                                     name='reg_op')(x5)


final_model = tf.keras.models.Model(inputs=model.input,outputs=[label_output,bbox_output])


def calculate_iou(y_true,y_pred):
  result = []
  for i in range(0,y_true.shape[0]):
    y_true = np.array(y_true,dtype=np.float32)
    y_pred = np.array(y_pred,dtype=np.float32)
    x_boxTrue_tleft = y_true[i,0]
    y_boxTrue_tleft = y_true[i,1]
    boxTrue_width = y_true[i,2]
    boxTrue_height = y_true[i,3]
    area_boxTrue = (boxTrue_width*boxTrue_height)

    #box_pred
    x_boxPred_tleft = y_pred[i,0]
    y_boxPred_tleft = y_pred[i,1]
    boxPred_width = y_pred[i,2]
    boxPred_height = y_pred[i,3]
    area_boxPred = (boxPred_width*boxPred_height)

    #calculating the bottom right co-ordinates
    x_boxTrue_br = x_boxTrue_tleft + boxTrue_width
    y_boxTrue_br = y_boxTrue_tleft + boxTrue_height

    x_boxPred_br = x_boxPred_tleft + boxPred_width
    y_boxPred_br = y_boxPred_tleft + boxPred_height

    #calculating the intersection box

    x_boxInt_tleft = np.max([x_boxTrue_tleft,x_boxPred_tleft])
    y_boxInt_tleft = np.max([y_boxTrue_tleft,y_boxPred_tleft])

    x_boxInt_br = np.min([x_boxTrue_br,x_boxPred_br])
    y_boxInt_br = np.min([y_boxTrue_br,y_boxPred_br])

    # calculating the area of intersection
    area_of_intersection = \
    np.max([0,(x_boxInt_br-x_boxInt_tleft)])*np.max([0,(y_boxInt_br-y_boxInt_tleft)])

    iou = area_of_intersection / ((area_boxTrue+area_boxPred)-area_of_intersection)

    iou = np.array(iou,dtype=np.float32)

    result.append(iou)

  return np.mean(result)


def IoU(y_true,y_pred):
  
  iou = tf.py_function(calculate_iou,[y_true,y_pred],tf.float32)
  return iou


final_model.compile(optimizer='adam',
                    loss = {'reg_op':'mse','class_op':'categorical_crossentropy'},
                    loss_weights={'reg_op':1,'class_op':1},
                    metrics={'reg_op':[IoU],'class_op':['accuracy']})


#model training
img_size = 224
batch_size = 32
train_generator=batch_generator(train_df_final,batch_size=32)
test_generator=batch_generator(test_df_final,batch_size=32)


def batch_generator(df,batch_size=32):
    while True:
      image_nums = np.random.randint(0,df.shape[0],size=batch_size)
      batch_images = np.zeros(shape=(batch_size,img_size,img_size,3))
      batch_labels = np.zeros(shape=(batch_size,196))
      batch_bboxes = np.zeros(shape=(batch_size,4))

      for i in range(batch_size):
        img=tf.keras.preprocessing.image.load_img(df.loc[image_nums[i],'file_path'],target_size=(img_size,img_size))
      
        img_array = tf.keras.preprocessing.image.img_to_array(img)
        batch_images[i] = img_array

        c1_label = df.loc[image_nums[i],'class']
        c1_label = tf.keras.utils.to_categorical(c1_label,196)
        batch_labels[i]=c1_label

        img_width = df.loc[image_nums[i],'width']
        img_height = df.loc[image_nums[i],'height']

        xmin = df.loc[image_nums[i],'x_min']*img_size/img_width
        xmax = df.loc[image_nums[i],'x_max']*img_size/img_width

        ymin = df.loc[image_nums[i],'y_min']*img_size/img_width
        ymax = df.loc[image_nums[i],'y_max']*img_size/img_width

        batch_bboxes[i]=[xmin,ymin,xmax-xmin,ymax-ymin]

      for i in range(batch_size):
        batch_images[i]=tf.keras.applications.vgg16.preprocess_input(batch_images[i])

      batch_bboxes=batch_bboxes/img_size

      yield batch_images,[batch_labels,batch_bboxes]


final_model.fit(train_generator,
                epochs=10,
                steps_per_epoch=train_df_final.shape[0]//batch_size,
                validation_data=test_generator,
                validation_steps=test_df_final.shape[0]//batch_size)

Epoch 1/10
251/251 [==============================] - 177s 555ms/step - loss: 2.2397 - class_op_loss: 2.0543 - reg_op_loss: 0.1854 - class_op_accuracy: 0.0839 - reg_op_IoU: 0.0045 - val_loss: 4.4847 - val_class_op_loss: 4.4620 - val_reg_op_loss: 0.0227 - val_class_op_accuracy: 0.0021 - val_reg_op_IoU: 0.0033
Epoch 2/10
251/251 [==============================] - 131s 525ms/step - loss: 2.4602 - class_op_loss: 2.4395 - reg_op_loss: 0.0207 - class_op_accuracy: 0.1066 - reg_op_IoU: 0.0029 - val_loss: 4.1736 - val_class_op_loss: 4.1523 - val_reg_op_loss: 0.0214 - val_class_op_accuracy: 0.0044 - val_reg_op_IoU: 0.0015
Epoch 3/10
251/251 [==============================] - 131s 523ms/step - loss: 2.4908 - class_op_loss: 2.4772 - reg_op_loss: 0.0136 - class_op_accuracy: 0.1182 - reg_op_IoU: 0.0026 - val_loss: 3.7833 - val_class_op_loss: 3.7622 - val_reg_op_loss: 0.0211 - val_class_op_accuracy: 0.0061 - val_reg_op_IoU: 7.4940e-04
Epoch 4/10
251/251 [==============================] - 130s 519ms/step - loss: 2.5808 - class_op_loss: 2.5699 - reg_op_loss: 0.0109 - class_op_accuracy: 0.1105 - reg_op_IoU: 0.0040 - val_loss: 3.9829 - val_class_op_loss: 3.8770 - val_reg_op_loss: 0.1059 - val_class_op_accuracy: 0.0019 - val_reg_op_IoU: 0.0049
Epoch 5/10
251/251 [==============================] - 127s 507ms/step - loss: 2.6098 - class_op_loss: 2.5820 - reg_op_loss: 0.0277 - class_op_accuracy: 0.0945 - reg_op_IoU: 0.0038 - val_loss: 3.6735 - val_class_op_loss: 3.6529 - val_reg_op_loss: 0.0206 - val_class_op_accuracy: 0.0072 - val_reg_op_IoU: 0.0014
Epoch 6/10
251/251 [==============================] - 127s 506ms/step - loss: 2.4430 - class_op_loss: 2.4318 - reg_op_loss: 0.0112 - class_op_accuracy: 0.0969 - reg_op_IoU: 0.0025 - val_loss: 3.1030 - val_class_op_loss: 3.0975 - val_reg_op_loss: 0.0055 - val_class_op_accuracy: 0.0128 - val_reg_op_IoU: 3.6207e-04
Epoch 7/10
251/251 [==============================] - 125s 499ms/step - loss: 2.2729 - class_op_loss: 2.2632 - reg_op_loss: 0.0097 - class_op_accuracy: 0.0938 - reg_op_IoU: 0.0024 - val_loss: 3.8202 - val_class_op_loss: 3.8138 - val_reg_op_loss: 0.0063 - val_class_op_accuracy: 0.0017 - val_reg_op_IoU: 2.1555e-05
Epoch 8/10
192/251 [=====================>........] - ETA: 14s - loss: 2.4330 - class_op_loss: 2.4238 - reg_op_loss: 0.0092 - class_op_accuracy: 0.1040 - reg_op_IoU: nan

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:37: RuntimeWarning: invalid value encountered in double_scalars

251/251 [==============================] - 119s 477ms/step - loss: 2.4210 - class_op_loss: 2.4113 - reg_op_loss: 0.0097 - class_op_accuracy: 0.1043 - reg_op_IoU: nan - val_loss: 3.5807 - val_class_op_loss: 3.5721 - val_reg_op_loss: 0.0086 - val_class_op_accuracy: 3.7351e-04 - val_reg_op_IoU: 4.1709e-05
Epoch 9/10
251/251 [==============================] - 119s 475ms/step - loss: 2.5540 - class_op_loss: 2.5359 - reg_op_loss: 0.0181 - class_op_accuracy: 0.1016 - reg_op_IoU: nan - val_loss: 3.5489 - val_class_op_loss: 3.5358 - val_reg_op_loss: 0.0131 - val_class_op_accuracy: 0.0055 - val_reg_op_IoU: 9.3779e-05
Epoch 10/10
251/251 [==============================] - 120s 478ms/step - loss: 2.5342 - class_op_loss: 2.5253 - reg_op_loss: 0.0089 - class_op_accuracy: 0.1023 - reg_op_IoU: nan - val_loss: 3.5933 - val_class_op_loss: 3.5120 - val_reg_op_loss: 0.0814 - val_class_op_accuracy: 0.0072 - val_reg_op_IoU: 0.0012

<tensorflow.python.keras.callbacks.History at 0x7fe2943889d0>

	file	x_min	y_min	x_max	y_max	class
0	00001.jpg	39	116	569	375	14
1	00002.jpg	36	116	868	587	3
2	00003.jpg	85	109	601	381	91
3	00004.jpg	621	393	1484	1096	134
4	00005.jpg	14	36	133	99	106
...	...	...	...	...	...	...
8139	08140.jpg	3	44	423	336	78
8140	08141.jpg	138	150	706	523	196
8141	08142.jpg	26	246	660	449	163
8142	08143.jpg	78	526	1489	908	112
8143	08144.jpg	20	240	862	677	17

	file	classification
0	03165.jpg	Acura TL Type-S 2008
1	03243.jpg	Acura TL Type-S 2008
2	07214.jpg	Acura TL Type-S 2008
3	05405.jpg	Acura TL Type-S 2008
4	07661.jpg	Acura TL Type-S 2008
...	...	...
8054	06013.jpg	Volvo XC90 SUV 2007
8055	00852.jpg	Volvo XC90 SUV 2007
8056	03307.jpg	Volvo XC90 SUV 2007
8057	04445.jpg	Volvo XC90 SUV 2007
8058	01513.jpg	Volvo XC90 SUV 2007

	file	classification
0	00183.jpg	Acura RL Sedan 2012
1	00249.jpg	Acura RL Sedan 2012
2	01856.jpg	Acura RL Sedan 2012
3	00347.jpg	Acura RL Sedan 2012
4	00849.jpg	Acura RL Sedan 2012
...	...	...
8036	07833.jpg	Volvo C30 Hatchback 2012
8037	00563.jpg	Volvo C30 Hatchback 2012
8038	05867.jpg	Volvo C30 Hatchback 2012
8039	00338.jpg	Volvo C30 Hatchback 2012
8040	02215.jpg	Volvo C30 Hatchback 2012

	file	x_min	y_min	x_max	y_max	class	classification
0	00001.jpg	39	116	569	375	14	Audi TTS Coupe 2012
1	00002.jpg	36	116	868	587	3	Acura TL Sedan 2012
2	00003.jpg	85	109	601	381	91	Dodge Dakota Club Cab 2007
3	00004.jpg	621	393	1484	1096	134	Hyundai Sonata Hybrid Sedan 2012
4	00005.jpg	14	36	133	99	106	Ford F-450 Super Duty Crew Cab 2012
...	...	...	...	...	...	...	...
8054	08140.jpg	3	44	423	336	78	Chrysler Town and Country Minivan 2012
8055	08141.jpg	138	150	706	523	196	smart fortwo Convertible 2012
8056	08142.jpg	26	246	660	449	163	Mercedes-Benz SL-Class Coupe 2009
8057	08143.jpg	78	526	1489	908	112	Ford GT Coupe 2006
8058	08144.jpg	20	240	862	677	17	Audi 100 Sedan 1994

	file	x_min	y_min	x_max	y_max	class	classification	file_path
0	00001.jpg	39	116	569	375	14	Audi TTS Coupe 2012	/content/drive/MyDrive/Capstone Data/Train Ima...
1	00002.jpg	36	116	868	587	3	Acura TL Sedan 2012	/content/drive/MyDrive/Capstone Data/Train Ima...
2	00003.jpg	85	109	601	381	91	Dodge Dakota Club Cab 2007	/content/drive/MyDrive/Capstone Data/Train Ima...
3	00004.jpg	621	393	1484	1096	134	Hyundai Sonata Hybrid Sedan 2012	/content/drive/MyDrive/Capstone Data/Train Ima...
4	00005.jpg	14	36	133	99	106	Ford F-450 Super Duty Crew Cab 2012	/content/drive/MyDrive/Capstone Data/Train Ima...
...	...	...	...	...	...	...	...	...
8054	08140.jpg	3	44	423	336	78	Chrysler Town and Country Minivan 2012	/content/drive/MyDrive/Capstone Data/Train Ima...
8055	08141.jpg	138	150	706	523	196	smart fortwo Convertible 2012	/content/drive/MyDrive/Capstone Data/Train Ima...
8056	08142.jpg	26	246	660	449	163	Mercedes-Benz SL-Class Coupe 2009	/content/drive/MyDrive/Capstone Data/Train Ima...
8057	08143.jpg	78	526	1489	908	112	Ford GT Coupe 2006	/content/drive/MyDrive/Capstone Data/Train Ima...
8058	08144.jpg	20	240	862	677	17	Audi 100 Sedan 1994	/content/drive/MyDrive/Capstone Data/Train Ima...

	file	x_min	y_min	x_max	y_max	class	classification
0	00001.jpg	30	52	246	147	181	Suzuki Aerio Sedan 2007
1	00002.jpg	100	19	576	203	103	Ferrari 458 Italia Convertible 2012
2	00003.jpg	51	105	968	659	145	Jeep Patriot SUV 2012
3	00004.jpg	67	84	581	407	187	Toyota Camry Sedan 2012
4	00005.jpg	140	151	593	339	185	Tesla Model S Sedan 2012
...	...	...	...	...	...	...	...
8036	08037.jpg	49	57	1169	669	63	Chevrolet Sonic Sedan 2012
8037	08038.jpg	23	18	640	459	16	Audi V8 Sedan 1994
8038	08039.jpg	33	27	602	252	17	Audi 100 Sedan 1994
8039	08040.jpg	33	142	521	376	38	BMW Z4 Convertible 2012
8040	08041.jpg	77	73	506	380	32	BMW X5 SUV 2007

	file	x_min	y_min	x_max	y_max	class	classification	file_path
0	00001.jpg	30	52	246	147	181	Suzuki Aerio Sedan 2007	/content/drive/MyDrive/Capstone Data/Test Imag...
1	00002.jpg	100	19	576	203	103	Ferrari 458 Italia Convertible 2012	/content/drive/MyDrive/Capstone Data/Test Imag...
2	00003.jpg	51	105	968	659	145	Jeep Patriot SUV 2012	/content/drive/MyDrive/Capstone Data/Test Imag...
3	00004.jpg	67	84	581	407	187	Toyota Camry Sedan 2012	/content/drive/MyDrive/Capstone Data/Test Imag...
4	00005.jpg	140	151	593	339	185	Tesla Model S Sedan 2012	/content/drive/MyDrive/Capstone Data/Test Imag...
...	...	...	...	...	...	...	...	...
8036	08037.jpg	49	57	1169	669	63	Chevrolet Sonic Sedan 2012	/content/drive/MyDrive/Capstone Data/Test Imag...
8037	08038.jpg	23	18	640	459	16	Audi V8 Sedan 1994	/content/drive/MyDrive/Capstone Data/Test Imag...
8038	08039.jpg	33	27	602	252	17	Audi 100 Sedan 1994	/content/drive/MyDrive/Capstone Data/Test Imag...
8039	08040.jpg	33	142	521	376	38	BMW Z4 Convertible 2012	/content/drive/MyDrive/Capstone Data/Test Imag...
8040	08041.jpg	77	73	506	380	32	BMW X5 SUV 2007	/content/drive/MyDrive/Capstone Data/Test Imag...